Snowflake data sharing with
Lyftrondata

Snowflake data sharing approach

Using your chosen account data model, Snowflake Data Sharing allows you to expose the relevant features across one or more accounts that are held by different companies. Each account will thus have the bare minimum of data necessary for its intended function. Snowflake offers a solution that lets you easily share your data with groups of people who need access, regardless of where it is hosted. To begin adding collaborators and setting access privileges, just set up a Data Sharing object within any table in your desired account and select the SHARES section from the menu on the left. Adapt the metadata of the container object to your particular requirements by editing it; if many accounts are involved, you can adjust the ownership time!

Shared data in the Snowflake Cost Table does not take up customer storage space, albeit this is a concept that may take some getting accustomed to. You won't see Snowflake table storage costs on your monthly bills as a result. Furthermore, since no data is duplicated, organizing and optimizing sharing may be done quickly and with ease, yielding immediate benefits.

Furthermore, datasets from data producers can be readily made available to a wide range of consumers by allowing data sharing from their Snowflake accounts. As opposed to this, businesses who buy data can quickly merge it with other datasets or information for additional examination and improvement before use. For example, candidate data containing relevant job applications might be accessible to staffing companies.

Users can transfer tables and the items they contain between databases using this feature. Similar to this, you could utilize native tools like BigQuery Transfer Tool to transfer complete BigQuery tables between clusters, allowing you to migrate entire databases within objects.

Snowflake data sharing with Lyftrondata

Lyftrondata provides its clients with three options: exchange, marketplace, and direct data share. The only thing separating the three offerings is their functionality; their technical foundations are all the same.

Direct data share

The most direct offering is direct data sharing. It indicates that the customer and the provider both have Snowflake accounts. Reader accounts are the consumers, and Snowflake accounts are the providers. The provider makes this; if the two accounts are in the same region, data is shared directly between them; if not, it is shared indirectly through replication. Without involving any additional steps, you can arrange direct data sharing on your own by following the official documentation.

Data marketplace

For your company's data repository, Snowflake (the account) serves as a centralized catalyst that lets you access and publish data without worrying about changing or replicating it. You can quickly become a provider and publish your data to the market with a Snowflake instance, and you can also easily become a consumer who can access the published data. Like the network administrator, Snowflake keeps everything safe.

Data exchange

While public data exchange refers to a centralized marketplace for the buying and selling of information, data exchange refers to private data sharing. A data repository that is solely accessible to individuals who are invited will be created by either choice.

Benefits of Lyftrondata’s data sharing facilities

Lyftrondata takes Snowflake Integration seriously in its endeavors to get over the drawbacks of the existing ETL tools, which stage flat files locally before sending them to Snowflake. Such a strategy causes additional pipeline delays and demands more disk space for data loading. Lyftrondata uses Snowflake's new data streaming features to load data to the Snowflake Data Warehouse in a novel way.

Your streaming database becomes an SQL data warehouse with the help of streaming transformation pushdown. There is no need to manage space for temporary files or pre-load any data because transformations are applied to the streamed records on the fly as they are written into the database once the data has been loaded. You can maintain continuous deployment of your streaming application while still enjoying all the benefits of an SQL data warehouse.

Some of the most common use cases that clients encounter are listed below. They are intended to provide you with inspiration for creating a data-sharing use case that satisfies your needs.

Information provided by an employment and sourcing firm

An on-demand staffing company that links employees with companies in need of their skills is the source of this use case. The company was able to connect 300,000 job openings with 77 million registered workers thanks to this platform. To compile datasets into a single database for analysis, the organization is currently investigating methods to move its data into Snowflake. With this method, the supplier can directly share a portion of its analytic data with its corporate client according to predetermined guidelines and standards.

Data sent between a technology and data company

Let us go back to the introduction's use case. This use case is for an analytical software supplier that, in addition to its analytics and insights technologies, provides its clients with a straightforward yet reliable method of ingesting and visualizing geolocation data. Its broad customer base, which spans all continents and nations, maintains track of millions of places worldwide. If clients want to connect it with other data sources, such as CRM or financial information, to assist them make more accurate business decisions, they can pick between the Flexible Data Sharing Plan and Regular Data Sharing. Here is how the data are shared:

• Production data is shared with STAGE
• STAGE has more source data loading into the table, validating the data loaded correctly
• STAGE shares the updated data with DEV.
• DEV needs to modify data by adding some transformations and then share it with PROD
• PROD shares its table TB1 with both stages via Share1
• STAGE creates a new table TBB using CTAS (Create Table As Select from TB1 of PROD)
• STAGE shares TBB to DEV via Share2
• DEV creates a new table TBD using CTAS (Create Table As Select from TBB of STAGE)
• DEV shares TBD to PROD via Share3
• PROD switches to using TBD of PROD instead of TB1
• PROD creates a new table TB1_V2 using CTAS (Create Table As Select from TBD of DEV)
• ALTER TABLE TB1 RENAME TO TB1_V1;
• ALTER TABLE TB1_V2 RENAME TO TB1;

How Lyftrondata drives powerful BI acceleration

The secure data governance system underpins Lyftrondata's data-sharing framework, enabling users with authorized access to view shared data instantly without requiring the original data to be moved. You can enhance your data governance and generate immediate insights and extra commercial value with this safe architecture.

With the help of Lyftrondata's cutting-edge architecture, users can now easily and securely exchange their data without writing a single line of code, thanks to row and column-level access security. Enabling governance on your data hub and effortlessly managing one-to-one, one-to-many, and many-to-many interactions with your partners and customers is made possible by Lyftrondata's strong and secure framework.

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